1 citations · 1 across the 3 of their papers we have counts for
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Optimizing Precision and Power by Machine Learning in Randomized Trials, with an Application to COVID-19
Nicholas Williams, Michael Rosenblum, Iván Díaz
The rapid finding of effective therapeutics requires the efficient use of available resources in clinical trials. The use of covariate adjustment can yield statistical estimates wi…
The Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods
Roy Adams, Suchi Saria, Michael Rosenblum
The use of observational time series data to assess the impact of multi-time point interventions is becoming increasingly common as more health and activity data are collected and…
Improving Precision through Adjustment for Prognostic Variables in Group Sequential Trial Designs: Impact of Baseline Variables, Short-Term Outcomes, and Treatment Effect Heterogeneity
Tianchen Qian, Michael Rosenblum, Huitong Qiu
In randomized trials, appropriately adjusting for baseline variables and short-term outcomes can lead to increased precision and reduced sample size. We examine the impact of such…
Model-Robust Inference for Clinical Trials that Improve Precision by Stratified Randomization and Covariate Adjustment
Bingkai Wang, Ryoko Susukida, Ramin Mojtabai +2
Two commonly used methods for improving precision and power in clinical trials are stratified randomization and covariate adjustment. However, many trials do not fully capitalize o…